Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs—A Preliminary Study.
Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconst...
| Publicado en: | Journal of Digital Imaging Vol. 37; no. 4; pp. 1960 - 1969 |
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| Autores principales: | , , , , , , , , |
| Formato: | diagnostic images research tables/charts Journal Article |
| Publicado: |
Springer Nature
Aug2024
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=179554118&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 179554118 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 08971889 DOQ jtl: Journal of Digital Imaging issn: 08971889 maglogo: N pubinfo: dt: Aug2024 vid: 37 iid: 4 pid: 237 pub: Springer Nature place: New York, New York artinfo: ui: 179554118 179554118 179554118 10.1007/s10278-024-01016-x 179554118 ppf: 1960 ppct: 9 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: Deep Learning Imaging Reconstruction Algorithm for Carotid Dual Energy CT Angiography: Opportunistic Evaluation of Cervical Intervertebral Discs—A Preliminary Study. aug: au: Jiang, Chenyu Zhang, Jingxin Li, Wenhuan Li, Yali Ni, Ming Jin, Dan Zhang, Yan Jiang, Liang Yuan, Huishu affil: https://ror.org/04wwqze12 Department of Radiology, Peking University Third Hospital, Beijing, China sug: subj: Deep Learning Image Processing, Computer Assisted Algorithms Tomography, X-Ray Computed Methods Intervertebral Disk Cervical Vertebrae Human Retrospective Design Record Review Descriptive Statistics Water Iodine Analysis Calcium Analysis Female Male Adult Middle Age Aged Funding Source Adult: 19-44 years Middle Aged: 45-64 years Aged: 65+ years Female Male ab: Thus, the aim of this study is to evaluate the performance of deep learning imaging reconstruction (DLIR) algorithm in different image sets derived from carotid dual-energy computed tomography angiography (DECTA) for evaluating cervical intervertebral discs (IVDs) and compare them with those reconstructed using adaptive statistical iterative reconstruction-Veo (ASiR-V). Forty-two patients who underwent carotid DECTA were included in this retrospective analysis. Three types of image sets (70 keV, water-iodine, and water-calcium) were reconstructed using 50% ASiR-V and DLIR at medium and high levels (DLIR-M and DLIR-H). The diagnostic acceptability and conspicuity of IVDs were assessed using a 5-point scale. Hounsfield Units (HU) and water concentration (WC) values of the IVDs; standard deviation (SD); and coefficient of variation (CV) were calculated. Measurement parameters of the 50% ASIR-V, DLIR-M, and DLIR-H groups were compared. The DLIR-H group showed higher scores for diagnostic acceptability and conspicuity, as well as lower SD values for HU and WC than the ASiR-V and DLIR-M groups for the 70 keV and water-iodine image sets (all p <.001). However, there was no significant difference in scores and SD among the three groups for the water-calcium image set (all p >.005). The water-calcium image set showed better diagnostic accuracy for evaluating IVDs compared to the other image sets. The inter-rater agreement using ASiR-V, DLIR-M, and DLIR-H was good for the 70 keV image set, excellent for the water-iodine and water-calcium image sets. DLIR improved the visualization of IVDs in the 70 keV and water-iodine image sets. However, its improvement on color-coded water-calcium image set was limited. pubtype: Academic Journal doctype: diagnostic images research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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